Could’s multi-agent system solve the headaches of travel?

mediumThis post was originally published by at Medium [AI]

Everytime we find ourselves stuck in traffic, frustrated at a delayed train or waiting for a taxi to show up, we think, “Surely there must be a better way of doing things?”

Well the answer to these everyday problems could be nearer than you think with’s multi-agent system that promises to do all the hard work for us and navigate the quickest and cheapest route from A to B. is an artificial intelligence company based in Cambridge that has developed a series of smart solutions to issues across the mobility and transport sectors by combining its in-depth knowledge of AI with decentralised distributed ledger technology.

Take taxis for example. The current model relies on the user opening their ride-hailing app of choice to request one of the drivers on the app’s network to take them to their destination. Both the consumer and the driver are beholden to that company in terms of the ownership of the data generated and the commissions charged.

In contrast, has developed a decentralised taxi network that takes the power away from the large, existing gig-economy operators and instead hands control to the people delivering the service. The platform combines AI, blockchain and cryptographic technologies to enable drivers and customers to be matched up without the involvement of a centralised third-party entity, offering a regular stream of demand for the drivers while providing the customers with a much broader range of drivers to quickly whisk them to their destination at the best available price.’s breakthrough solution has come about due to a unique alignment of the three technologies underpinning its system. Most significantly, cryptographic technology has reached a point of maturity that it is now usable rather than purely theoretical.

Major breakthroughs in verifiable credentials, which allow assertions to be verified in a machine readable, self-service way ensure that key certifications, such as insurance and licences, can be independently verified, giving passengers complete confidence about the safety of their ride.

Each driver and individual customer has an autonomous agent acting on their behalf, that understands individual preferences. These agents are able to communicate with each other on a peer-to-peer basis. Agents can use’s decentralised search-and-discovery mechanism to find other agents that are relevant to them, given desires and context. In the case of ride-hailing, this acts as an economic dating agency: given all the constraints, it connects a passenger to the best-fit driver. These constraints may include what the driver is prepared to do, the size of the vehicle, and the suggested price. The agents can then negotiate a deal and the potential options can be presented to the user. Once accepted, the process of managing payment, disputes and action progress are guided by the agents and governed digitally by smart-contacts.

Transactions are placed on a blockchain ledger, providing a permanent, non-modifiable record of all activity. Coupled with machine learning and digital identities, this information is also used to provide repulation — delivering another layer of incentives for all on the network, both service providers and those that use it, to build this through good behaviour. Ratings, tied to the proof-of-service delivered, can also be stored on this ledger building a more comprehensive trust picture that all network users are able to access without the permission of others.

“When the gig economy first arrived, it looked like it would empower people but central entities killed that,’’ said Toby Simpson, Chief Operating Officer of in a recent blog. “We’re armed with a unique opportunity now to build a decentralised delivery network that’s owned by those who use and provide the service. By redistributing currently centralised margins to the individuals taking part, we solve many of the issues associated with transitioning from a luxury to a utility, especially with regulation and margins increasing their effects on the gig economy.’’

The same technologies utilised in this taxi example have also been developed by to eliminate fruitless searches for a parking space, reduce congestion in cities as well as linking up journeys on public transport to optimise journeys.

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This post was originally published by at Medium [AI]

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